Pub Date : 2023-12-11DOI: 10.3390/agriengineering5040147
Jesús Aidmir Yeikame Morelia-Jiménez, B. Montaño-Leyva, F. Blancas-Benitez, Luz del Carmen Romero-Islas, P. Gutiérrez-Martínez, L. Hernández-Montiel, P. U. Bautista-Rosales, R. González-Estrada
Crown rot, caused by Fusarium species, is the most devastating postharvest disease in bananas. Fungicides are traditionally applied as a postharvest treatment to control crown rot in bananas. However, there is a need to research environmentally friendly compounds as postharvest treatments instead of chemical fungicides. The phenolic compounds gallic acid, protocatechuic acid, and chlorogenic acid were identified in coconut mesocarp extract. Overall, the treatments were more efficient in crown-based than fruit-based culture mediums. The mycelial development was inhibited in a range from 20 to 26% (applying coconut mesocarp extract at 5%) compared to the control. Sporulation and spore germination were significantly inhibited, with a reduction of 88% in spore production and 91% in spore germination inhibition compared to the control. In in vivo tests, the aqueous extracts were effective by limiting the percentage of infected fruit, crown rot, and fruit severity. The use of coconut mesocarp extracts can be an effective and environmentally friendly alternative to the use of fungicides for controlling Fusarium musae on bananas.
{"title":"Coconut Mesocarp Extracts to Control Fusarium musae, the Causal Agent of Banana Fruit and Crown Rot","authors":"Jesús Aidmir Yeikame Morelia-Jiménez, B. Montaño-Leyva, F. Blancas-Benitez, Luz del Carmen Romero-Islas, P. Gutiérrez-Martínez, L. Hernández-Montiel, P. U. Bautista-Rosales, R. González-Estrada","doi":"10.3390/agriengineering5040147","DOIUrl":"https://doi.org/10.3390/agriengineering5040147","url":null,"abstract":"Crown rot, caused by Fusarium species, is the most devastating postharvest disease in bananas. Fungicides are traditionally applied as a postharvest treatment to control crown rot in bananas. However, there is a need to research environmentally friendly compounds as postharvest treatments instead of chemical fungicides. The phenolic compounds gallic acid, protocatechuic acid, and chlorogenic acid were identified in coconut mesocarp extract. Overall, the treatments were more efficient in crown-based than fruit-based culture mediums. The mycelial development was inhibited in a range from 20 to 26% (applying coconut mesocarp extract at 5%) compared to the control. Sporulation and spore germination were significantly inhibited, with a reduction of 88% in spore production and 91% in spore germination inhibition compared to the control. In in vivo tests, the aqueous extracts were effective by limiting the percentage of infected fruit, crown rot, and fruit severity. The use of coconut mesocarp extracts can be an effective and environmentally friendly alternative to the use of fungicides for controlling Fusarium musae on bananas.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"7 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.3390/agriengineering5040145
I. Yulita, Muhamad Farid Ridho Rambe, A. Sholahuddin, A. S. Prabuwono
The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run.
{"title":"A Convolutional Neural Network Algorithm for Pest Detection Using GoogleNet","authors":"I. Yulita, Muhamad Farid Ridho Rambe, A. Sholahuddin, A. S. Prabuwono","doi":"10.3390/agriengineering5040145","DOIUrl":"https://doi.org/10.3390/agriengineering5040145","url":null,"abstract":"The primary strategy for mitigating lost productivity entails promptly, accurately, and efficiently detecting plant pests. Although detection by humans can be useful in detecting certain pests, it is often slower compared to automated methods, such as machine learning. Hence, this study employs a Convolutional Neural Network (CNN) model, specifically GoogleNet, to detect pests within mobile applications. The technique of detection involves the input of images depicting plant pests, which are subsequently subjected to further processing. This study employed many experimental methods to determine the most effective model. The model exhibiting a 93.78% accuracy stands out as the most superior model within the scope of this investigation. The aforementioned model has been included in a smartphone application with the purpose of facilitating Indonesian farmers in the identification of pests affecting their crops. The implementation of an Indonesian language application is a contribution to this research. Using this local language makes it easier for Indonesian farmers to use it. The potential impact of this application on Indonesian farmers is anticipated to be significant. By enhancing pest identification capabilities, farmers may employ more suitable pest management strategies, leading to improved crop yields in the long run.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"73 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.3390/agriengineering5040143
H. Rodrigues, M. B. Ceddia, G. Vasques, Vera L. Mulder, G. Heuvelink, Ronaldo P. Oliveira, Z. Brandão, J. P. S. Morais, Matheus L. Neves, Sílvio R. L. Tavares
The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.
{"title":"Remote Sensing and Kriging with External Drift to Improve Sparse Proximal Soil Sensing Data and Define Management Zones in Precision Agriculture","authors":"H. Rodrigues, M. B. Ceddia, G. Vasques, Vera L. Mulder, G. Heuvelink, Ronaldo P. Oliveira, Z. Brandão, J. P. S. Morais, Matheus L. Neves, Sílvio R. L. Tavares","doi":"10.3390/agriengineering5040143","DOIUrl":"https://doi.org/10.3390/agriengineering5040143","url":null,"abstract":"The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"64 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.3390/agriengineering5040144
Fatema Al Kindi, Talal Al-Shukaili, P. Pathare, Farooq Al Jahwari, N. Al-Azri, Ohood Al Ghadani
In this study, the finite volume method was used to evaluate the thermal performance of a flat-plate solar collector used to dry agricultural crops. A 3D numerical model was created and used to predict the outlet air velocities and temperatures for three inlet air velocities. When compared with experimental measurements, the numerical predictions showed good agreement under all testing conditions. Then, the numerical model was used to predict the internal airflow and heat transfer characteristics of the collector. The internal baffles were found to increase the dwell time and efficiency but also promote flow separation, which resulted in flow loss. In addition, the collector has a transparent cover that results in a substantial heat loss, which can be mitigated by adding a vacuum gap between the flow inside the collector and the cover. Increasing the flow rate increased the heat loss and decreased the heat uptake, which decreased the temperature difference between the inlet and outlet of the collector. Because the heat was lost through long-wavelength radiation via the transparent cover and sidewalls, coating the absorber plate with black matte paint to increase the solar radiation absorption coefficient did not improve the drying performance.
{"title":"Thermal Performance of a Flat-Plate Solar Collector for Drying Agricultural Crops","authors":"Fatema Al Kindi, Talal Al-Shukaili, P. Pathare, Farooq Al Jahwari, N. Al-Azri, Ohood Al Ghadani","doi":"10.3390/agriengineering5040144","DOIUrl":"https://doi.org/10.3390/agriengineering5040144","url":null,"abstract":"In this study, the finite volume method was used to evaluate the thermal performance of a flat-plate solar collector used to dry agricultural crops. A 3D numerical model was created and used to predict the outlet air velocities and temperatures for three inlet air velocities. When compared with experimental measurements, the numerical predictions showed good agreement under all testing conditions. Then, the numerical model was used to predict the internal airflow and heat transfer characteristics of the collector. The internal baffles were found to increase the dwell time and efficiency but also promote flow separation, which resulted in flow loss. In addition, the collector has a transparent cover that results in a substantial heat loss, which can be mitigated by adding a vacuum gap between the flow inside the collector and the cover. Increasing the flow rate increased the heat loss and decreased the heat uptake, which decreased the temperature difference between the inlet and outlet of the collector. Because the heat was lost through long-wavelength radiation via the transparent cover and sidewalls, coating the absorber plate with black matte paint to increase the solar radiation absorption coefficient did not improve the drying performance.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"36 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138595581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.3390/agriengineering5040142
Evandro Menezes de Oliveira, Sheila Tavares Nascimento, J. Mós, Lenilson da Fonseca Roza, Juliana Beatriz Toledo, T. C. dos Santos
This study was conducted to survey the level of technification of quail sheds in Brazil. Data from 25 quail farms (5 in each Brazilian region) were collected by image analysis of videos available on the Internet. The analyzed variables were farm location, degree of technological adoption in quail sheds, housing conditions, structural conditions, wall conditions, and thermal comfort equipment. The data were subjected to descriptive analysis, and differences were assessed using the chi-squared test (p < 0.10). It was found that curtain walls were the most used system for air entry and renewal in quail sheds. Fan systems were present in only 12% of sheds, and evaporative cooling systems (or air conditioning) were observed in 4% of sheds, exclusively on large farms. Internal insulation was used in 20.83% of farms. In conclusion, Brazilian quail sheds have a low degree of technification; about 90% do not use implements such as ceiling, ventilation, and cooling systems. These conditions make it difficult to control environmental variables within quail sheds, impairing thermal comfort and, consequently, animal welfare and quail productivity.
{"title":"Thermal Conditions of Laying Quail Sheds in Brazil","authors":"Evandro Menezes de Oliveira, Sheila Tavares Nascimento, J. Mós, Lenilson da Fonseca Roza, Juliana Beatriz Toledo, T. C. dos Santos","doi":"10.3390/agriengineering5040142","DOIUrl":"https://doi.org/10.3390/agriengineering5040142","url":null,"abstract":"This study was conducted to survey the level of technification of quail sheds in Brazil. Data from 25 quail farms (5 in each Brazilian region) were collected by image analysis of videos available on the Internet. The analyzed variables were farm location, degree of technological adoption in quail sheds, housing conditions, structural conditions, wall conditions, and thermal comfort equipment. The data were subjected to descriptive analysis, and differences were assessed using the chi-squared test (p < 0.10). It was found that curtain walls were the most used system for air entry and renewal in quail sheds. Fan systems were present in only 12% of sheds, and evaporative cooling systems (or air conditioning) were observed in 4% of sheds, exclusively on large farms. Internal insulation was used in 20.83% of farms. In conclusion, Brazilian quail sheds have a low degree of technification; about 90% do not use implements such as ceiling, ventilation, and cooling systems. These conditions make it difficult to control environmental variables within quail sheds, impairing thermal comfort and, consequently, animal welfare and quail productivity.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"60 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.3390/agriengineering5040140
Gloria Alexandra Ortiz, Adrian Nicolas Chamorro, Jhon Fabio Acuña-Caita, I. López-Cruz, E. Villagran
Modeling and simulation have become fundamental tools for the microclimatic analysis of greenhouses under various climatic conditions. These models allow precise control of the climate inside the structures and the optimization of their performance under any situation. In Colombia, the availability of energy balance models adapted to local greenhouses and their climate is limited, which affects the decision-making of both technical advisors and growers. This study focused on calibrating and evaluating a dynamic energy balance model to predict the thermal behavior of an innovative type of plastic-covered greenhouse designed for the Bogotá savanna. The selected model considers fundamental heat and mass transfer processes, incorporating parameters that depend on the architecture of the structure and local climatic conditions, making it suitable for protected agriculture in Colombia. The results of the post-calibration evaluation showed that the model is highly accurate, with a temperature prediction efficiency close to 86%. This ensures that the model can accurately predict the thermal behavior of the greenhouse being evaluated. It is important to note that the model can also anticipate phenomena characteristics of Colombian greenhouses, such as thermal inversion. This advance has become a valuable tool for decision-making in protected agriculture in the region.
{"title":"Calibration and Implementation of a Dynamic Energy Balance Model to Estimate the Temperature in a Plastic-Covered Colombian Greenhouse","authors":"Gloria Alexandra Ortiz, Adrian Nicolas Chamorro, Jhon Fabio Acuña-Caita, I. López-Cruz, E. Villagran","doi":"10.3390/agriengineering5040140","DOIUrl":"https://doi.org/10.3390/agriengineering5040140","url":null,"abstract":"Modeling and simulation have become fundamental tools for the microclimatic analysis of greenhouses under various climatic conditions. These models allow precise control of the climate inside the structures and the optimization of their performance under any situation. In Colombia, the availability of energy balance models adapted to local greenhouses and their climate is limited, which affects the decision-making of both technical advisors and growers. This study focused on calibrating and evaluating a dynamic energy balance model to predict the thermal behavior of an innovative type of plastic-covered greenhouse designed for the Bogotá savanna. The selected model considers fundamental heat and mass transfer processes, incorporating parameters that depend on the architecture of the structure and local climatic conditions, making it suitable for protected agriculture in Colombia. The results of the post-calibration evaluation showed that the model is highly accurate, with a temperature prediction efficiency close to 86%. This ensures that the model can accurately predict the thermal behavior of the greenhouse being evaluated. It is important to note that the model can also anticipate phenomena characteristics of Colombian greenhouses, such as thermal inversion. This advance has become a valuable tool for decision-making in protected agriculture in the region.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"112 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138609587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.3390/agriengineering5040138
Pavel A. Dmitriev, B. Kozlovsky, A. A. Dmitrieva, Vladimir S. Lysenko, Vasiliy A. Chokheli, Tatyana V. Varduni
Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data.
{"title":"Indication of Light Stress in Ficus elastica Using Hyperspectral Imaging","authors":"Pavel A. Dmitriev, B. Kozlovsky, A. A. Dmitrieva, Vladimir S. Lysenko, Vasiliy A. Chokheli, Tatyana V. Varduni","doi":"10.3390/agriengineering5040138","DOIUrl":"https://doi.org/10.3390/agriengineering5040138","url":null,"abstract":"Hyperspectral imaging techniques are widely used to remotely assess the vegetation and physiological condition of plants. Usually, such studies are carried out without taking into account the light history of the objects (for example, direct sunlight or light scattered by clouds), including light-stress conditions (photoinhibition). In addition, strong photoinhibitory lighting itself can cause stress. Until now, it is unknown how light history influences the physiologically meaningful spectral indices of reflected light. In the present work, shifts in the spectral reflectance characteristics of Ficus elastica leaves caused by 10 h exposure to photoinhibitory white LED light, 200 μmol photons m−2 s−1 (light stress), and moderate natural light, 50 μmol photons m−2 s−1 (shade) are compared to dark-adapted plants. Measurements were performed with a Cubert UHD-185 hyperspectral camera in discrete spectral bands centred on wavelengths from 450 to 950 nm with a 4 nm step. It was shown that light stress leads to an increase in reflection in the range of 522–594 nm and a decrease in reflection at 666–682 nm. The physiological causes of the observed spectral shifts are discussed. Based on empirical data, the light-stress index (LSI) = mean(R666:682)/mean(R552:594) was calculated and tested. The data obtained suggest the possibility of identifying plant light stress using spectral sensors that remotely fix passive reflection with the need to take light history into account when analysing hyperspectral data.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":"23 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.3390/agriengineering5040139
A. Hayajneh, Sahel Batayneh, Eyad Alzoubi, Motasem Alwedyan
Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML.
{"title":"TinyML Olive Fruit Variety Classification by Means of Convolutional Neural Networks on IoT Edge Devices","authors":"A. Hayajneh, Sahel Batayneh, Eyad Alzoubi, Motasem Alwedyan","doi":"10.3390/agriengineering5040139","DOIUrl":"https://doi.org/10.3390/agriengineering5040139","url":null,"abstract":"Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138616397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01DOI: 10.3390/agriengineering5040137
Josipa Lisičar Vukušić, Thomas Millenautzki, S. Barbe
The objectives set in the European Green Deal constitute the starting point of this review, which then focuses on the current implementation gap between agro-industrial wastes as resources for large-scale bioprocesses (e.g., baker’s yeast, bioethanol, citric acid, and amino acids). This review highlights the current lack of sustainability of the post-harvest processing of grapes and apples. In light of the European Green Deal, industrial biotechnology often lacks sustainability as well. We reviewed the recent progress reported in the literature to enhance the valorization of grape and apple pomace and the current failure to implement this research in technical processes. Nevertheless, selected recent papers show new perspectives to bridge this gap by establishing close collaborations between academic teams and industrial partners. As a final outcome, for the first time, we drew a circular flow diagram that connects agriculture post-harvest transformation with the industrial biotechnology and other industries through the substantial valorization of apple and grape pomace into renewable energy (solid biofuels) and sugar extracts as feedstock for large-scale bioprocesses (production of baker’s yeast industry, citric acid, bioethanol and amino acids). Finally, we discussed the requirements needed to achieve the successful bridging of the implementation gap between academic research and industrial innovation.
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The flavor, aroma, and color of coffee can be changed due to mechanical damage, reducing its quality. To measure the mechanical behavior of the fruit, compression tests can be performed at different stages of ripeness. In this study, we analyzed the deformation, strain energy, and von Mises stress of coffee fruits at mature, semi-mature, and immature stages under compression forces. Compression in three directions (x, y, and z) was simulated on coffee fruit models using the finite element method. A compression support was applied in the opposite direction to the force application axis. Numerical simulations of the compression process allowed us to verify that the more mature the fruit, greater the associated mean deformation (2.20 mm mm−1, 0.78 mm mm−1, and 0.88 mm mm−1), the lower the mean strain energy (0.07 mJ, 0.21 mJ, and 0.34 mJ), and the lower the mean equivalent von Mises stress (0.25 MPa, 1.03 MPa, and 1.25 MPa), corresponding to ripe, semi-ripe, and immature fruits, respectively. These analyses not only save time and professional resources but also offer insights into how strain energy and von Mises stress affect fruits at different maturation stages. This information can guide machine adjustments to reduce coffee harvesting damages.
咖啡的风味、香气和颜色会因机械损伤而改变，从而降低其质量。为了测量水果的力学性能，可以在不同的成熟阶段进行压缩试验。本研究分析了咖啡果实成熟、半成熟和未成熟阶段在压缩力作用下的变形、应变能和von Mises应力。采用有限元法对咖啡果模型进行了x、y、z三个方向的压缩模拟。在作用力施加轴的相反方向上施加压缩支撑。压缩过程的数值模拟验证了果实越成熟，相应的平均变形(2.20 mm mm−1、0.78 mm mm−1和0.88 mm mm−1)越大，平均应变能(0.07 mJ、0.21 mJ和0.34 mJ)越低，平均等效von Mises应力(0.25 MPa、1.03 MPa和1.25 MPa)越低，分别对应于成熟、半成熟和未成熟果实。这些分析不仅节省了时间和专业资源，而且提供了应变能和von Mises应力如何影响不同成熟阶段果实的见解。这些信息可以指导机器调整，以减少咖啡收获的损害。
{"title":"Modeling of Coffee Fruit: An Approach to Simulate the Effects of Compression","authors":"Janielle Souza Pereira, Ricardo Rodrigues Magalhães, Fábio Lúcio Santos, Ednilton Tavares de Andrade, Leomar Santos Marques","doi":"10.3390/agriengineering5040141","DOIUrl":"https://doi.org/10.3390/agriengineering5040141","url":null,"abstract":"The flavor, aroma, and color of coffee can be changed due to mechanical damage, reducing its quality. To measure the mechanical behavior of the fruit, compression tests can be performed at different stages of ripeness. In this study, we analyzed the deformation, strain energy, and von Mises stress of coffee fruits at mature, semi-mature, and immature stages under compression forces. Compression in three directions (x, y, and z) was simulated on coffee fruit models using the finite element method. A compression support was applied in the opposite direction to the force application axis. Numerical simulations of the compression process allowed us to verify that the more mature the fruit, greater the associated mean deformation (2.20 mm mm−1, 0.78 mm mm−1, and 0.88 mm mm−1), the lower the mean strain energy (0.07 mJ, 0.21 mJ, and 0.34 mJ), and the lower the mean equivalent von Mises stress (0.25 MPa, 1.03 MPa, and 1.25 MPa), corresponding to ripe, semi-ripe, and immature fruits, respectively. These analyses not only save time and professional resources but also offer insights into how strain energy and von Mises stress affect fruits at different maturation stages. This information can guide machine adjustments to reduce coffee harvesting damages.","PeriodicalId":7846,"journal":{"name":"AgriEngineering","volume":" 107","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138620416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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